Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects

Table 17

G-measure analysis by each TF-ML technique on individual dataset.

Technique	AR1	AR3	CM1	KC2	KC3	MW1	PC1	PC2	PC3	PC4
CDT	?	?	0	0.7171	0.6562	0.6502	0.7128	?	0.4072	0.7184
CS-Forest	0.33	0.5584	0.3854	0.6471	?	0.4291	0.546	0.2856	0.5212	0.8607
DS	0	0.8244	?	0.6447	0.6492	0.5125	?	?	?	?
Forest-PA	0	0.766	0	0.7453	0	0	0.7966	?	0.6041	0.8007
HT	?	0.531	?	0.7347	0	?	?	?	?	0.8104
J48	0.4381	0.6497	0.2953	0.6765	0.571	0.6114	0.6917	?	0.5927	0.7038
LMT	0	0.6424	0.2467	0.7752	0.5509	0.8096	0.37	0	0.3103	0.7959
RF	0	0.8141	0.4192	0.7263	0.6245	0.6182	0.7276	?	0.691	0.8019
RT	0.3948	0.6497	0.3506	0.664	0.3214	0.3366	0.5253	0.08	0.4324	0.6086
REP-T	?	0.6391	0	0.6924	0.5169	0.3289	0.7257	?	0.589	0.7403